iManage: Policy-driven self-management for enterprise-scale systems

Abstract

It is obvious that big, complex enterprise systems are hard to manage. What is not obvious is how to make them more manageable. Although there is a growing body of research into system self-management, many techniques are either too narrow, focusing on a single component rather than the entire system, or not robust enough, failing to scale or respond to the full range of an administrator's needs. In our iManage system we have developed a policy-driven system modeling framework that aims to bridge the gap between manageable components and manageable systems. In particular, iManage provides: (1) system statespace partitioning, which divides a large system state-space into partitions that are more amenable to constructing system models and developing policies, (2) online model and policy adaptation to allow the self-management infrastructure to deal gracefully with changes in operating environment, system configuration, and workload, and (3) tractability and trust, where tractability allows an administrator to understand why the system chose a particular policy and also influence that decision, and trust allows an administrator to understand the system's confidence in a proposed, automated action. Simulations driven by scenarios given to us by our industrial collaborators demonstrate that iManage is effective both at constructing useful system models and in using those models to drive automated system management. © IFIP International Federation for Information Processing 2007.